Plant disease detection android application

Inspiration

Farmers in India face problems while not being able to discern one disease from other and end up being using wrong pesticides or fungicides, or even not knowing which type of problem their crops are facing. This problem is solved using our application which will directly tell what kind of disease the plant is facing and what to use to solve the problem.

What it does

Our application for plant disease detection using AI and machine learning (ML) utilizes computer vision and image recognition technology to analyze images of plants and identify signs of disease. The application is trained on a dataset of labeled images of diseased and healthy plants in order to learn the characteristics of different diseases.The application is used to quickly and accurately diagnose plant diseases in the field, helping farmers and agricultural professionals to protect their crops and improve yield. It also contains various products like pesticides, fungicides, bactericides etc which the user can buy directly.

How we built it

Collect and label data: Gather a large dataset of images of plants with various diseases, as well as healthy plants. Label the data with the corresponding disease or condition. Pre-processing: Clean and pre-process the data to make it suitable for training a ML model. This may include resizing images, removing noise, and splitting the data into training, validation, and test sets. Train model: Use the labeled data to train a ML model. Validate model: Use the validation set to evaluate the performance of the trained model and make any necessary adjustments. Deploy the model: Once the model is trained and validated, deploy it to the app, so that it can be used by users to diagnose plant diseases. Testing: Test the app with real-world images of plant diseases to see how well the model performs in practice. Improve the model: Use the feedback from the testing phase to improve the model and make it more accurate.

Convert the model to TensorFlow Lite format: Use the TensorFlow Lite Converter to convert the model from its original format (e.g. TensorFlow, Keras) to TensorFlow Lite format. Add the TensorFlow Lite library to the app: Add the TensorFlow Lite library to the app's build.gradle file. This will enable the app to use the TensorFlow Lite library to run the model. Load the model into the app: Use the TensorFlow Lite Interpreter to load the model into the app. The Interpreter is responsible for executing the model on the device. Prepare the input data: Prepare the input data in the format that the model expects. This may include resizing images, normalizing values, and converting data types. Run the model: Use the Interpreter to run the model on the input data. The Interpreter will return the output of the model, which can be used to make predictions or decisions. Display the results: Display the results of the model in the app, by showing a label or an image with the cure for disease.

Challenges we ran into

The most difficult challenge we faced was to integrate android application with ML model. We solved this problem using TensorFlow Lite library.

Accomplishments that we're proud of

The most proud accomplishment would be completing the project within 24 hours time limit. As it was our first hackathon, we were'nt confident enough to complete the entire project within the time limit, but it became possible with an amazing teamwork and joint effort by the whole team

What we learned

The most important things we learned during the hackathon was teamwork, cooperation and mainly how to complete a project within a given timeline that too under pressure.

What's next for Plant disease detection android application

The future scope for an application for plant disease detection using AI and ML is quite promising. As technology continues to improve, the accuracy and speed of the application will likely increase, making it an even more valuable tool for farmers and agricultural professionals. One potential area of growth for this application is the integration of more advanced technologies such as drones and sensor networks to gather data on crop health. This would allow for more comprehensive monitoring of crops and early detection of diseases, which can help to prevent outbreaks and protect crops on a larger scale. Another possible future development is the integration of other data sources such as weather data, soil data, and satellite data to create a more holistic view of the crop's health and environment. This will allow for a better understanding of the factors that contribute to plant diseases and help to predict the spread of diseases. Additionally, the application can be extended to other crops and also other kinds of plants such as trees and ornamental plants. This will help to improve the overall understanding of plant health and aid in the development of new products and services. Lastly, with the increasing trend of precision agriculture and the need for sustainable agricultural practices, the application can play a vital role in providing insights to farmers and decision-makers to make data-driven decisions.